io.github.hsouf/cldf-mcp
PYPI · CLDF-MCP
WALS typology joined with Glottolog classification, with provenance and family-aware counts.
Available components
Last scanned (2 hours ago)
Scores higher than 89% of PyPI servers
Category Education and Research
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
- 1 of 28 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency97
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Cryptographically verified build provenance (signed, bound to hsouf/cldf-mcp). View diagnostics → Pass
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 10 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability82
- 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).Pass
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 1371 tokens (~114/item across 12 items; 10 tools + 2 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management37
- Stability observed for 11 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage97
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 90% of tool parameters carry a description.Partial
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 10 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 12 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 5 Nov 26 +15
- Malware scan: unverified → pass ▲ security
- 4 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 30 to 33. That category is still filling its 30-day observation window: 9 days of observed history at the previous scan, 10 at this one. The score rises as the window fills, whether or not the server changes.
- 2 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.
- 31 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.
- 30 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- Security disclosure: unverified → fail ▼ functional
- 29 Oct 26 +1
- Security disclosure: fail → unverified ▼ functional
- 28 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- 27 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 3 to 7. That category is still filling its 30-day observation window: 1 days of observed history at the previous scan, 2 at this one. The score rises as the window fills, whether or not the server changes.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Analysed pypi/cldf-mcp@0.1.0
Provenance Verified
A signed build attestation was found and verified, binding this exact artifact to the source repository it claims to come from.
| Result | Verified |
|---|---|
| Ecosystem | pypi |
| Reason | Verified |
| Discovered via | Registry attestation endpoint |
| Source repo | hsouf/cldf-mcp |
| Certificate issuer | https://token.actions.githubusercontent.com |
| Certificate SAN | https://github.com/hsouf/cldf-mcp/.github/workflows/publish.yml@refs/tags/v0.1.0 |
| Rekor log index | 3461887871 |
| Predicate type | PyPI publish attestation https://docs.pypi.org/attestations/publish/v1 |
| Subject digest | sha256:392922eeb6045fe8f86f2e59db46e7af5f273fcee7503b041917092ad9724425 |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 28 packages
| Packages resolved | 28 |
|---|---|
| No linked repository | 1 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
classification ~66
Glottolog classification of a language or group: lineage, children (with how many dataset entries fall under each), sisters, and the number of languages below.
| Name | Type | Req | Description |
|---|---|---|---|
| language | string | yes | Language, family or subgroup: name or Glottocode. |
| max_children | integer | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
crosstab ~175
Cross-tabulate two features over the languages coded for both. Each cell gives languages, families and genera attesting the combination and the share of its largest family, so you can tell a cross-linguistic tendency from the signature of one big family. Use this to check implicational universals.
| Name | Type | Req | Description |
|---|---|---|---|
| by_macroarea | boolean | – | Also give each cell per macroarea. |
| family | – | – | Restrict to a Glottolog family or subgroup. |
| feature_a | string | yes | First feature ID, e.g. 'wals:81A'. |
| feature_b | string | yes | Second feature ID, e.g. 'wals:85A'. |
| macroarea | – | – | Restrict to one macroarea. |
| sample | – | – | Restrict to 'wals100' or 'wals200'. |
Structured output declared, but exposes no named fields.
No examples provided.
filter_languages ~142
Find languages matching feature conditions, e.g. SOV order AND postpositions. Returns match counts against the number of languages coded for all conditions (the denominator), with family and genus counts and the families contributing most matches, plus the matching languages.
| Name | Type | Req | Description |
|---|---|---|---|
| conditions | array | yes | All conditions must hold (AND). Combine features across datasets freely. |
| family | – | – | Restrict to a Glottolog family or subgroup, or Glottocode. |
| limit | integer | – | Maximum languages listed (counts are always complete). |
| macroarea | – | – | Restrict to one macroarea. |
| sample | – | – | Restrict to 'wals100' or 'wals200'. |
Structured output declared, but exposes no named fields.
No examples provided.
find_language ~122
Find languages by name or code. Returns Glottocodes, family, macroarea and which dataset entries have data. Use this before other tools when a name might be ambiguous (e.g. 'Arabic' is a family; WALS has Arabic varieties). Candidates are ranked: exact codes, exact names, alternate names, then partial matches.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | – |
| query | string | yes | Language name (any spelling or alternate name), ISO 639-3 code, Glottocode, or dataset code like 'wals:bsq'. |
Structured output declared, but exposes no named fields.
No examples provided.
get_feature ~149
Describe one feature: definition, chapter and authors, and how its values are distributed, counted by languages, families and genera.
| Name | Type | Req | Description |
|---|---|---|---|
| by_macroarea | boolean | – | Also break counts down by macroarea. |
| family | – | – | Restrict to a Glottolog family or subgroup ('Austronesian', 'Germanic') or Glottocode. |
| feature | string | yes | Feature ID, e.g. 'wals:81A'. |
| macroarea | – | – | Restrict to one macroarea: Africa, Australia, Eurasia, North America, Papunesia, South America. |
| sample | – | – | Restrict to a balanced sample: 'wals100' or 'wals200'. |
Structured output declared, but exposes no named fields.
No examples provided.
get_source ~74
Full bibliographic references for source IDs cited in datapoints.
| Name | Type | Req | Description |
|---|---|---|---|
| dataset | – | – | Dataset the IDs come from, e.g. 'wals'. |
| source_ids | array | yes | Source IDs as returned by get_values, e.g. ['Nekitel-1985'] (page suffixes like '[94]' are accepted). |
Structured output declared, but exposes no named fields.
No examples provided.
get_values ~154
Look up specific datapoints: the value of each feature for each language, with sources, comments and full citations. Unresolvable names are reported in 'errors' with candidates instead of failing the call. When several dataset entries share a Glottocode (e.g. Basque and Basque dialects), the main entry is used and the others are listed; pass a dataset ref like 'wals:bso' to pick a specific one.
| Name | Type | Req | Description |
|---|---|---|---|
| features | array | yes | Feature IDs, e.g. ['wals:81A', 'wals:85A']. |
| languages | array | yes | Languages: names, Glottocodes, ISO codes or dataset refs ('wals:bsq'). |
Structured output declared, but exposes no named fields.
No examples provided.
language_profile ~81
Everything known about one language: Glottolog identity, classification, endangerment and description status, plus all its feature values per dataset.
| Name | Type | Req | Description |
|---|---|---|---|
| dataset | – | – | Only show this dataset's values. |
| language | string | yes | Name, Glottocode, ISO 639-3 code or dataset ref. |
| limit | integer | – | Maximum values per dataset. |
Structured output declared, but exposes no named fields.
No examples provided.
list_datasets ~27
List the loaded datasets with their release versions, coverage, licenses and how to cite them.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
search_features ~89
Search typological features by keyword. Returns feature IDs, names, possible values and how many languages are coded for each.
| Name | Type | Req | Description |
|---|---|---|---|
| dataset | – | – | Restrict to one dataset, e.g. 'wals'. |
| limit | integer | – | – |
| query | string | yes | Keywords such as 'word order', 'negation', 'tone', 'plural', or a feature ID like '81A'. |
Structured output declared, but exposes no named fields.
No examples provided.
How do I install the io.github.hsouf/cldf-mcp server?
io.github.hsouf/cldf-mcp runs locally as a PyPI package, launched with uvx cldf-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · cldf-mcp
claude mcp add hsouf-cldf-mcp -- uvx cldf-mcp
What is the io.github.hsouf/cldf-mcp server?
io.github.hsouf/cldf-mcp is listed in the public MCP registry as io.github.hsouf/cldf-mcp. WALS typology joined with Glottolog classification, with provenance and family-aware counts. This page covers its PyPI package (cldf-mcp).
Is the io.github.hsouf/cldf-mcp server safe to use?
io.github.hsouf/cldf-mcp scores 87 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 2026. Its build provenance is signed and verified. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the io.github.hsouf/cldf-mcp server expose?
io.github.hsouf/cldf-mcp exposes 10 tools: list_datasets, find_language, search_features, get_feature, get_values, and 5 more. Their descriptions and schemas cost roughly 1,079 tokens of context every time the server is loaded.
Is the io.github.hsouf/cldf-mcp server still maintained?
io.github.hsouf/cldf-mcp is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the io.github.hsouf/cldf-mcp server under?
io.github.hsouf/cldf-mcp declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.